Real-time recognition of audio content is being increasingly used in various applications. For example, the name, artist, and/or album of an unknown song may be identified or a particular film may be identified from its soundtrack. ACR algorithms include acoustic fingerprinting, which works by analyzing a segment of audio (music, film, etc.) and generating what are known as fingerprints from characteristics of the audio. To recognize a piece of audio content, an audio fingerprint generated from the piece of audio is compared to a database of reference audio fingerprints.
There are several acoustic fingerprinting algorithms that are in widespread use today. A common example is the Shazaam app which identifies the song that a user is listening by using acoustic fingerprinting. Other examples include Gracenote and MusicBrainz.
Conventionally, however, audio content recognition has been limited at least in that conventional schemes have not provided the ability to detect or align timing of the content, i.e., the current time position with respect to the overall time of the content. Other limitations of conventional audio content recognition include the relative large size of the fingerprints, the relative complexity of the content recognition process, and the resulting delay in content recognition.